class_docstr: Selects instances by constructing a lightweight coreset [BLK18]_ over document embeddings.
      
class_docstr_tok: ["Selects","instances","by","constructing","a","lightweight","coreset","[","BLK18","]","_","over","document","embeddings","."]
docstr: Parameters
  ----------
  normalize : bool
      Embeddings will be normalized before the coreset construction if True.
docstr_tok: ["Parameters","--","--","--","--","--","normalize",":","bool","Embeddings","will","be","normalized","before","the","coreset","construction","if","True","."]
code: def __init__(self, normalize=True):
      """
      Parameters
      ----------
      normalize : bool
          Embeddings will be normalized before the coreset construction if True.
      """
      self.normalize = normalize
  
file_path: small-text/small_text/query_strategies/coresets.py
imports: import warnings
  import numpy
  from sklearn.metrics import pairwise_distances
  from small_text.query_strategies.strategies import EmbeddingBasedQueryStrategy
label_desc: Use image node_id 1 to create a new LightweightCoreset object from inherited base classes: EmbeddingBasedQueryStrategy with example: obj = LightweightCoreset(normalize)